paradex_filters_model
Get detailed schema information to build precise data filters for Paradex trading tools. Understand available fields, data types, and formats to construct accurate JMESPath queries and filter expressions.
Instructions
Get detailed schema information to build precise data filters.
Use this tool when you need to:
- Understand exactly what fields are available for filtering
- Learn the data types and formats for specific fields
- Build complex JMESPath queries with correct syntax
- Create sophisticated filtering and sorting expressions
Knowing the exact schema helps you construct precise filters that
return exactly the data you need, avoiding trial and error.
Example use cases:
- Learning what fields exist in market data responses
- Finding the correct property names for filtering
- Understanding data types for numerical comparisons
- Building complex multi-criteria filters for large datasetsInput Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| tool_name | Yes | The name of the tool to get the filters for. |
Implementation Reference
- src/mcp_paradex/tools/market.py:25-55 (handler)The handler function `get_filters_model` decorated with `@server.tool(name="paradex_filters_model")`. It takes a `tool_name` parameter and returns the JSON schema (model_json_schema()) of the corresponding data model for various Paradex tools, enabling precise JMESPath filtering.
@server.tool(name="paradex_filters_model") async def get_filters_model( tool_name: Annotated[str, Field(description="The name of the tool to get the filters for.")], ) -> dict: """ Get detailed schema information to build precise data filters. Use this tool when you need to: - Understand exactly what fields are available for filtering - Learn the data types and formats for specific fields - Build complex JMESPath queries with correct syntax - Create sophisticated filtering and sorting expressions Knowing the exact schema helps you construct precise filters that return exactly the data you need, avoiding trial and error. Example use cases: - Learning what fields exist in market data responses - Finding the correct property names for filtering - Understanding data types for numerical comparisons - Building complex multi-criteria filters for large datasets """ tool_descriptions = { "paradex_markets": models.MarketDetails.model_json_schema(), "paradex_market_summaries": models.MarketSummary.model_json_schema(), "paradex_open_orders": models.OrderState.model_json_schema(), "paradex_orders_history": models.OrderState.model_json_schema(), "paradex_vaults": models.Vault.model_json_schema(), "paradex_vault_summary": models.VaultSummary.model_json_schema(), } return tool_descriptions[tool_name] - src/mcp_paradex/tools/market.py:25-25 (registration)The @server.tool decorator registers the `get_filters_model` function as the MCP tool named 'paradex_filters_model'.
@server.tool(name="paradex_filters_model") - Pydantic input schema definition for the tool using Annotated Field for validation and description.
tool_name: Annotated[str, Field(description="The name of the tool to get the filters for.")],